Abstract
A novel method based on Supervised Locality Preserving Projection(SLPP)and False Nearest Neighbor(FNN)was proposed for selecting the most proper feature for nonlinear pattern classification.In the proposed method,nonlinear original data were mapped to the supervised locality preserving subspace to eliminate the existing multi-collinearity among the features.Then,the interpretation capability for original features was estimated through calculating the variable mapping distance in the supervised locality preserving subspace.The nearest neighbor classifier based on each subset obtained by eliminating weak features successively was constructed.Finally,the optimalfeature subset was selected corresponding to the highest recognition accuracy and the least number of features.The experiment on synthetic dataset shows that the proposed method can obtain an optimal feature subset containing the essential features in accordance with the classification goal.The method was used to select the features of low resistivity hydrocarbon reservoir,and the result indicates that the obtained optimal feature subset contains over 50% less feature and achieves 8% higher recognition accuracy as compared to that of the all-feature set.These results validate that the proposed method can offer excellent abilities of original feature selection and nonlinear feature selection.
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